An electrochemical system is a system that either derives electrical energy from chemical reactions, or facilitates chemical reactions through the introduction of electrical energy. An electrochemical system generally includes a cathode, an anode, and an electrolyte, and is typically complex with multiple heterogeneous subsystems, multiple scales from nanometers to meters. Examples of these systems include fuel cells, batteries, and electroplating systems. On-line characterization of batteries or fuel cells in vehicles is difficult, due to very rough noisy environments. The problems have emerged in recent years with the significant expanding market of battery-powered vehicles, and consequently the public concerns of battery safety and battery life calendars.
Although there may be many kinds of characterization models for an electrochemical system, equivalent circuit models are most appropriate in many applications where stringent real-time requirements and limiting computing powers need to be considered. An algorithm for a circuit model is relatively simple, meaning that simulation time is short and the computation cost is relatively low. A circuit model is an empirical model that describes the electrochemical system with a resistor-capacitor (or resistor-inductor-capacitor) circuit.
In a suitable circuit model, major effects of thermodynamic and kinetic processes in the electrochemical system can be represented by circuit elements. For example, the electrode potential between the cathode and the anode of a system can be represented with a voltage source, the charge-transfer processes can be represented with charge-transfer resistances, the double-layer adsorption can be represented with capacitances, and mass-transfer or diffusion effects can be represented with resistances such as Warburg resistances. Therefore a circuit model is extremely useful for many on-line diagnostics of the real-time states of an electrochemical system.
On-line and real-time characterization of such electrochemical systems is desirable in many applications, which include real-time evaluation of in-flight batteries on a satellite or aviation vehicle, and dynamic diagnostics of fraction batteries for electric and hybrid-electric vehicles. In many battery-powered systems, the efficiency of batteries can be greatly enhanced by intelligent management of the electrochemical energy storage system. Management is only possible with proper diagnosis of the battery states.
As the transportation industry strives to electrify vehicles with Li-ion batteries, on-line battery diagnostics becomes more essential due to critical issues such as battery safety, operation range, and calendar life. Among all the electrochemistry tools that can be used for battery diagnostics, electrochemical impedance spectroscopy (EIS) is an indispensable tool. This is because most battery failure modes such as electrolyte corrosion, hard and soft (dendritic) shorts, and thermal runaways can be traced by their signatures in the impendence spectroscopy. Reliable battery operation needs accurate estimation of the electrode voltages, which is correlated to the accurate estimation of the battery parameters. Background information regarding EIS can be found in Buller et al., “Impedance-based simulation models of supercapacitors and Li-ion batteries for power electronic applications,” IEEE Transactions on Industry Applications, vol. 41, no. 3, pp. 742-747 (2005).
The present state of art of EIS, however, has limitations for on-line battery characterization. Since the typical design is mainly focused on accurate impedance measurements at the charge-transfer interface, the signals used for driving a battery and their mathematical methods are not applicable for on-line characterization. For example, the driving signals used in the prior art are generally small, so that the resultant polarization potentials of the electrodes are small in a linear region. However, in vehicle application, batteries are sometimes in charge depletion mode; the mathematical method employed in most EIS methods in the art is frequency-domain analysis. It provides high signal-noise-ratio but is not applicable for on-line characterization.
The dynamic performance of a battery is determined not only by the charge-transfer reaction at the interface, but also by the other factors, such as mass transfer (diffusion) effects in solid phases. The amplitudes of prior-art profiles are typically small in order to guarantee that the reactions are around equilibrium states of the system. In calculating the dynamic parameters, such as impedances, prior art typically works in the frequency domain and employs a non-iterative procedure.
New methods are needed to apply impedance analysis in time domain aiming for the application of on-line diagnostics. In view of the shortcomings in the art, improved methods for characterizing electrochemical systems are needed. These methods, and the apparatus and systems to implement them, preferably are able to broadly accept various exciting signals, are stable and robust against noises, and are agile for real-time use.